In this course, you'll get to grips with the modern ELT approach and discover how dbt plays a central role in the modern data stack. After a brief introduction, you'll quickly get hands-on with setting up your own dbt project. You'll learn how dbt helps structure SQL transformations, add tests, and automatically generate documentation. We also cover the differences between dbt Core and dbt Cloud, collaborating via Git, and integrating dbt into CI/CD processes. Through practical exercises, you'll build an end-to-end data model and gain insight into how larger organisations manage analytics pipelines with dbt.

What you'll learn

• The core principles of dbt and the modern ELT approach.
• How to use dbt to structure and test SQL transformations.
• Automatically documenting and visualising your data models.
• Working with Jinja, macros, and references between models.
• How dbt integrates with Git and CI/CD.

Programme

Part 1 – Introduction to the Modern Data Stack

• The difference between ETL and ELT, and the role of dbt.

Part 2 – Your First dbt Project

• Setting up, configuring, and running models.

Part 3 – Testing and Documentation

• Data quality, documentation, and lineage.

Part 4 – Advanced Features

• Macros, Jinja, and model dependencies.

Part 5 – Integration with Git & CI/CD

• Deployment, workflows, and collaboration.

Part 6 – Best Practices & Q&A

• Patterns, tips, and next steps.

• Data engineers and analytics engineers.
• BI specialists and data analysts looking to professionalise their SQL transformations.
• Anyone who wants to structure and automate data workflows within a modern data stack.

Prerequisites

• Basic knowledge of SQL.
• Some familiarity with data warehouses or data modelling is a plus.